Not so long ago, the job of product manager was about assessing market data, creating requirements, and managing the hand-off to sales/marketing. Maybe you’d talk to a customer somewhere in there and they’d tell you what features they wanted. But companies that manage product that way are dying.
Being a product person today is a new game, and product managers are at the center of it. Today, particularly if your product is mostly digital, you might update it several times a day. Massive troves of data are available for making decisions and, at the same time, deep insights into customer motivation and experience are more important than ever. The job of the modern product manager is to charter a direction and create a successful working environment for all the actors involved in product success. It’s not a simple job or an easy job, but it is a meaningful job where you’ll be learning all the time.
This course will help you along your learning journey and prepare you with the skills and perspective you need to:
Create the actionable focus to successfully manage your product (week 1)
Focus your work using modern product management methods (week 2)
Manage new products and explore new product ideas (week 3)
Manage and amplify existing products (week 4)
This course is ideal for current product or general managers interested in today's modern product management methods.
This course was developed with the generous support of the Batten Institute at UVA’s Darden School of Business. The Batten Institute’s mission is to improve the world through entrepreneurship and innovation: www.batteninstitute.org.

NK

This course covers all the necessary and much required fundamentals about the product management. The course structure is very well balanced and covers all the aspects of product management. Thanks

JW

May 23, 2019

Filled StarFilled StarFilled StarFilled StarFilled Star

I'm totally a Newbie about Digital Product Management. I learn a lot of New Things. I hope this Certificate will advance my career. Thank you Professor Alex Cowan for teaching me a lot of things.

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Achieving Focus and #Winning

As a product manager, you and your team will always have more ideas and more requests than you can possibly manage. Making matters still more complicated, part of your job is to develop and then ‘sell’ a particular view of what should happen with your product to diverse stakeholders (engineering, marketing, etc.). How do you do it? Success requires an actionable, testable focus. The successful product manager identifies vivid, testable customer outcomes and creates focus around them. This week we’ll show you how to interface effectively with all your stakeholders to create that actionable focus.

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Alex Cowan

Faculty & Batten Fellow

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In this video, we're going to talk about how to create a successful interface with your data science team. Your inputs to the data science team are not that different than the inputs you would give to development team. They are vivid descriptions of the customer journey and the customer experience, probably more so with the product as it is and of course data. Hopefully, coming from your own product or your own infrastructure, your touchpoints with the customer because you can't do data science without data. And back to you, the data scientists are going to try things and give you inferences and maybe even some ideas on how to act on those. You'll try those out and that's how you create a robust interface here with your data science team, is through experimentation. Because even though it's called data science, it is like any science experiment. You're going to try things that work out well and move the needle for you and you're going to try things out that don't. You need data to do data science. So, one failure what you want to avoid is that, you're not collecting any data off your product or your touchpoint because that will make it really hard. It'll create certainly fewer opportunities for data science team to do something valuable for you. My advice to you is to pay special attention to acting on the inferences that data science team gives you and iterating on those and trying them out. Because even if early on, they're sort of rough and you're unable to use them that well, this is such an important area and such an important learning opportunity for you and your career that I think this is an area, I would ascribe special importance to even if it's not on your A list at the moment. The data science process we have in here, we have questions of interest and an idea of what's the user experience, how does the user interact with both the product and kind of like let's say our support system or all the different touch points that we have with them. And from here, we get insights and hopefully those are actionable for us and then we're going to see if we can move the needle with those and we're going to iterate through these things and try different stuff. And one of the interesting things about the practice of data science is you have to be able to kind of move forward and move backward. By forward, I mean the best way to get a good experimental result is to go into it with a nice strong hypothesis and that is where these inputs come into play, questions of interest, vivid depictions of user experience. The way that you move backward is a little less obvious. This is something in fact I've been learning about myself. This is a case that we use here at Darden to teach data science called movie lens. This is a plot of two factors. Factor one is here, and factor two is here and these are movies. Like this says Dungeons and Dragons. This says Jackass the Movie. And this is a plot created by the machine intelligence about the variables, the attributes of the movies that are most likely to explain people's different preferences for them. So, for instance, this might be a scale of you know happy to sad and this might be scary to not scary. I don't think that's actually what these things are but this is the kind of output from your data science team that you might need to kind of work on with them a little bit to understanding human terms, to make actionable for next step and testing. You need to make sure that you're collecting data off of your product. Your data science team will help you with that but let's walk through a simple example. Let's say there is this company Enable Quiz and they offer lightweight quizzing solutions for companies that hire a lot of engineers, so that the HR manager and the hiring manager can assess whether somebody is really skilled in something, really experienced in something or not. And then through that they can assess the fit with the organization. So let's say Enable Quiz, we have inside the company, we have this customer or user of the HR manager, whose job it is to get candidates and the hiring manager who is ultimately going to be hiring these people. These two worked together inside the company and then we have job candidates and we provide a quiz that they use to assess the fit. So, what data might the product manager of this product collect from the customer? Well, one idea is you know for forgiving these quizzes, we might want to make sure that we're instrumenting and we have ready access to the pass rates on the individual questions. Because a really, really unusually high pass rate might mean the question is too easy or too obvious and an extraordinarily low pass rate might mean that the question is too hard or poorly worded or just plain wrong and you want to fix it. That one is an easy one because the instructional design team here, we get this data from the learning platforms that we use. What else might be interesting? While this company is in the general space of skills assessment. So, they might want to know that for a given type of job that they're screening candidates for, what skills do they select to put into their quiz. Because let's say the quiz can have multiple skills in it. Ruby on Rails, Linux systems administration, whatever. If they collect that and they have data on the success rate of different hires. They might be able to proactively suggest to their customers what skills and what kind of quizzes they might want to make for a given job description they could analyze and that would be really great. Anything that makes the customer's job easier is really great. If we go back to the Google Analytics example, if Google Analytics can accurately predict exactly what I have to do with my website to make it perform better. Well, that's a much more valuable product than a product that need to dig through a lot of analysis and make my own inferences by hand with. And that's what so exciting about the future of data science among other things. So, we've talked about how to create a successful interface with your data science team, we've talked about some of the activities you guys might undertaking your collaboration.